5 Gleason score-associated gene signatures serve as novel biomarkers for identifying early recurring events and contributing to early diagnosis for Prostate Adenocarcinoma

Abstract Background: Prostate cancer (PCa) recurrence leads to much higher mortality than those without recurring events. Early and accurate laboratory diagnosis is particularly important for early identification of patients at high risk of recurrence and to benefit from additional systemic intervention. This study aimed to develop efficient and accurate Prostate Cancer diagnostic and prognostic biomarkers for the identification of initial tumor new events. Methods: Gene Expression Omnibus (GEO) datasets and The Cancer Genome Atlas (TCGA) data portal were utilized to obtain differentially expressed genes (DEGs) and clinical trait information in PCa. WGCNA analysis obtained the most relevant clinical traits and genes enriched in several modules. Univariate Cox regression analysis and multivariate Cox proportional hazards (Cox-PH) model was employed to candidate gene signatures related to Disease-Free Interval (DFI). Internal and external cohort was utilized to test and validate the validity, accuracy, and clinical utility of prognostic models.Results: We constructed and optimized a valid and credible model for predicting patient outcomes, based on 5 Gleason score-associated gene signatures (ZNF695, CENPA, TROAP, BIRC5, KIF20A). Furthermore, ROC and Kaplan-Meier analysis revealed higher diagnostic efficiency for PCa and predictive effectiveness in tumor recurrence and metastasis. Calibration curve also revealed high prediction accuracy in internal TCGA cohort and external GEO cohort. The model was prognostically significant in the stratified cohort, including TNM classification and Gleason score, and was deemed to be an independent PCa prognostic factor, and superioring to other clinicopathological characteristics. On the other hand, we also measured the correlation between gene signatures’ expression and inflammation landscape. 5 gene signatures were significantly positively correlated with tumor purity and negatively correlated with the immersion levels of CD8+ T cells. Conclusions: Our study identified and validated 5 gene signatures as biomarkers for prostate cancer diagnosis, providing an assessment of DFI while predicting tumor progression, possibly providing novel theories for the treatment of prostate cancer.

Download Full-text

Identification and validation of a prognostic 5-protein signature for biochemical recurrence following radical prostatectomy in prostate cancer

10.21203/rs.3.rs-29488/v1 ◽

2020 ◽

Author(s):

Xiangkun Wu ◽

Wenjie Li ◽

Daojun Lv ◽

Yongda Liu ◽

Di Gu

Keyword(s):

Prostate Cancer ◽

Regression Analysis ◽

Biochemical Recurrence ◽

Cox Regression ◽

Characteristic Curve ◽

Cox Proportional Hazards ◽

The Cancer Genome Atlas ◽

Medical Decision ◽

Cox Regression Analysis ◽

Protein Signature

Abstract Background : Biochemical recurrence (BCR) is considered as an indicator for prostate cancer (PCa)-specific recurrence and mortality. However, lack of effective prediction model to assess the prognosis of patients for optimization of treatment. The aim of this work was to construct a protein-based nomogram that could predict BCR for PCa.Materials and methods: Univariate Cox regression analysis was conducted to identify candidate proteins from the Cancer Genome Atlas (TCGA) database. LASSO Cox regression was further conducted to pick out the most significant prognostic proteins and formulate the proteins signature for predicting BCR. Additionally, a nomogram was constructed by multivariate Cox proportional hazards regression.Results: We established a 5‐protein-based signature which was well used to identify PCa patients into high‐ and low‐risk groups. Kaplan-Meier analysis demonstrated patients with higher BCR generally had significantly worse survival than those with lower BCR (p<0.0001). Time-dependent receiver operating characteristic curve expounded that ours signature had excellent prognostic efficiency for 1‐, 3‐ and 5‐year BCR (area under curve in training set: 0.691, 0.797, 0.808 and 0.74, 0.739, 0.82 in the test set). Univariable and multivariate Cox regression analysis showed that this 5‐protein signature was an independent of several clinical signatures including age, Gleason score, T stage, N status, PSA and residual tumor. Moreover, a nomogram was constructed and calibration plots confirmed the its predictive value in 3-, 5- and 10-year BCR overall survival.Conclusion: Our study identified a 5-protein-based signature and constructed a prognostic nomogram that reliably predicts BCR in prostate cancer. The findings might be of paramount importance in tumor prognosis and medical decision-making.

Download Full-text

Identification and Validation of a Prognostic 5-Protein Signature for Biochemical Recurrence Following Radical Prostatectomy for Prostate Cancer

Frontiers in Surgery ◽

10.3389/fsurg.2021.665115 ◽

2021 ◽

Vol 8 ◽

Author(s):

Daojun Lv ◽

Zanfeng Cao ◽

Wenjie Li ◽

Haige Zheng ◽

Xiangkun Wu ◽

...

Keyword(s):

Prostate Cancer ◽

Survival Analysis ◽

Biochemical Recurrence ◽

Cox Regression ◽

Characteristic Curve ◽

The Cancer Genome Atlas ◽

Medical Decision ◽

Regression Analyses ◽

Cox Regression Analysis ◽

Protein Signature

Background: Biochemical recurrence (BCR) is an indicator of prostate cancer (PCa)-specific recurrence and mortality. However, there is a lack of an effective prediction model that can be used to predict prognosis and to determine the optimal method of treatment for patients with BCR. Hence, the aim of this study was to construct a protein-based nomogram that could predict BCR in PCa.Methods: Protein expression data of PCa patients was obtained from The Cancer Proteome Atlas (TCPA) database. Clinical data on the patients was downloaded from The Cancer Genome Atlas (TCGA) database. Lasso and Cox regression analyses were conducted to select the most significant prognostic proteins and formulate a protein signature that could predict BCR. Subsequently, Kaplan–Meier survival analysis and Cox regression analyses were conducted to evaluate the performance of the prognostic protein-based signature. Additionally, a nomogram was constructed using multivariate Cox regression analysis.Results: We constructed a 5-protein-based prognostic prediction signature that could be used to identify high-risk and low-risk groups of PCa patients. The survival analysis demonstrated that patients with a higher BCR showed significantly worse survival than those with a lower BCR (p < 0.0001). The time-dependent receiver operating characteristic curve showed that the signature had an excellent prognostic efficiency for 1, 3, and 5-year BCR (area under curve in training set: 0.691, 0.797, 0.808 and 0.74, 0.739, 0.82 in the test set). Univariate and multivariate analyses indicated that this 5-protein signature could be used as independent prognosis marker for PCa patients. Moreover, the concordance index (C-index) confirmed the predictive value of this 5-protein signature in 3, 5, and 10-year BCR overall survival (C-index: 0.764, 95% confidence interval: 0.701–0.827). Finally, we constructed a nomogram to predict BCR of PCa.Conclusions: Our study identified a 5-protein-based signature and constructed a nomogram that could reliably predict BCR. The findings might be of paramount importance for the prediction of PCa prognosis and medical decision-making.Subjects: Bioinformatics, oncology, urology.

Download Full-text

Five lncRNAs Associated With Prostate Cancer Prognosis Identified by Coexpression Network Analysis

Technology in Cancer Research & Treatment ◽

10.1177/1533033820963578 ◽

2020 ◽

Vol 19 ◽

pp. 153303382096357

Author(s):

Xiaoyong Gong ◽

Bobin Ning

Keyword(s):

Prostate Cancer ◽

Network Analysis ◽

Cox Regression ◽

Characteristic Curve ◽

Interaction Network ◽

The Cancer Genome Atlas ◽

Cancer Prognosis ◽

Normal Prostate ◽

Cox Regression Analysis ◽

Incidence And Mortality

Prostate cancer (PCa) is a highly malignant tumor, with increasing incidence and mortality rates worldwide. The aim of this study was to identify the prognostic lncRNAs and construct an lncRNA signature for PCa diagnosis by the interaction network between lncRNAs and protein-coding genes (PCGs). The differentially expressed lncRNAs (DElncRNAs) and PCGs (DEPCGs) between PCa and normal prostate tissues were screened from The Cancer Genome Atlas (TCGA) database. The DEPCGs were functionally annotated in terms of the enriched pathways. Weighted gene co-expression network analysis (WGCNA) of 104 PCa samples identified 15 co-expression modules, of which the Turquoise module was negatively correlated with cancer and included 5 key lncRNAs and 47 PCGs. KEGG pathway analyses of the core 47 PCGs showed significant enrichment in classic PCa-related pathways, and overlapped with the enriched pathways of the DEPCGs. LINC00857, LINC00900, LINC00908, LINC00900, SNHG3 and FENDRR were significantly associated with the survival of PCa and have not been reported previously. Finally, Multivariable Cox regression analysis was used to establish a prognostic risk formula, and the patients were accordingly stratified into the low- and high-risk groups. The latter had significantly worse OS compared to the low-risk group (P < 0.01), and the area under the receiver operating characteristic curve (ROC) of 14-year OS was 0.829. The accuracy of our prediction model was determined by calculating the corresponding concordance index (C-index) and risk curves. In conclusion, we established a 5-lncRNA prognostic signature that provides insights into the biological and clinical relevance of lncRNAs in PCa.

Download Full-text

Active Surveillance in Younger Men With Prostate Cancer

Journal of Clinical Oncology ◽

10.1200/jco.2016.68.0058 ◽

2017 ◽

Vol 35 (17) ◽

pp. 1898-1904 ◽

Cited By ~ 31

Author(s):

Michael S. Leapman ◽

Janet E. Cowan ◽

Hao G. Nguyen ◽

Katsuto K. Shinohara ◽

Nannette Perez ◽

...

Keyword(s):

Prostate Cancer ◽

Active Surveillance ◽

Gleason Score ◽

Cox Regression ◽

Young Patients ◽

Definitive Treatment ◽

Primary Study ◽

Study Objective ◽

Cox Regression Analysis ◽

Younger Age

Purpose The suitability of younger patients with prostate cancer (PCa) for initial active surveillance (AS) has been questioned on the basis of eventual treatment necessity and concerns of safety; however, the role of age on surveillance outcomes has not been well defined. Patients and Methods We identified men managed with AS at our institution with a minimum follow-up of 6 months. The primary study objective was to examine the association of age with risk of biopsy-based Gleason score upgrade during AS. We also examined the association of age with related end points, including overall biopsy-determined progression, definitive treatment, and pathologic and biochemical outcomes after delayed radical prostatectomy (RP), using descriptive statistics, the Kaplan-Meier method, and multivariable Cox proportional hazards regression. Results A total of 1,433 patients were followed for a median of 49 months; 74% underwent initial biopsy at a referring institution. Median age at diagnosis was 63 years, including 599 patients (42%) ≤ 60 years old and 834 (58%) > 60 years old. The 3- and 5-year biopsy-based Gleason score upgrade-free rates were 73% and 55%, respectively, for men ≤ 60 years old compared with 64% and 48%, respectively, for men older than 60 years ( P < .01). On Cox regression analysis, younger age was independently associated with lower risk of biopsy-based Gleason score upgrade (hazard ratio per 1-year decrease, 0.969 [95% CI, 0.956 to 0.983]; P < .01), and persisted upon restriction to men meeting strict AS inclusion criteria. There was no significant association between younger age and risk of definitive treatment or risk of biochemical recurrence after delayed RP. Conclusion Younger patient age was associated with decreased risk of biopsy-based Gleason score upgrade during AS but not with risk of definitive treatment in the intermediate term. AS represents a strategy to mitigate overtreatment in young patients with low-risk PCa in the early term.

Download Full-text

Chromogranine A and neuron-specific enolase as the main predictors of survival for hormone-refractory prostate cancer (HRPC) patients

Journal of Clinical Oncology ◽

10.1200/jco.2007.25.18_suppl.15594 ◽

2007 ◽

Vol 25 (18_suppl) ◽

pp. 15594-15594

Author(s):

A. Banu ◽

E. Banu ◽

D. Dionysopoulos ◽

J. Medioni ◽

F. Scotte ◽

...

Keyword(s):

Prostate Cancer ◽

Regression Analysis ◽

Gleason Score ◽

Cox Regression ◽

Neuron Specific Enolase ◽

Cox Regression Analysis ◽

Risk Of Death ◽

Metastatic Sites ◽

Ecog Ps ◽

The Impact

15594 Background: Clinical studies suggested that the extent of neuro-endocrine differentiation in prostate cancer increases with tumor progression and the development of androgen refractory status. Chromogranine (CgA) and neuron-specific enolase (NSE) are currently explored as surrogate markers. Methods: Eligible chemonaive HRPC patients (pts) were required to have an ECOG performance status (PS) ≤ 2. Before chemotherapy initiation, we quantified NSE, CgA and PSA in the venous blood using commercial kits. We evaluated the impact of baseline NSE, CgA and PSA on overall survival (OS) using multivariate Cox regression analysis, stratified by chemotherapy regimen. Secondary, we studied the correlation between NSE, CgA, PSA and other important variables as age, Gleason score, hemoglobin, number of metastatic sites and ECOG PS. Results: Data of 39 consecutive HRPC pts treated between December 01–06 in a single French center were analyzed. Chemotherapy was docetaxel-based in 92% of pts. Median age was 71 years (range 51–86) and 79% of pts had bone metastases. Elevated NSE, CgA and PSA were observed in 6, 9 and 30% of pts and median levels were 10.8, 67 and 23.3 ng/mL, respectively. Gleason 8–10 was present in 49% of pts. Significant correlations were observed between NSE and the number of metastatic sites and between CgA and age, hemoglobin and ECOG PS. The baseline PSA was only correlated with Gleason score. Median OS for the entire cohort was 24.4 months (95% CI, 18.8–29.9). Two-year OS was 15% and only 19% of patients are dead. Univariate Cox regression analysis showed only a significant relationship between OS and baseline NSE: hazard ratio= 1.09 (95% CI, 1.03–1.16), P=0.006. No other known prognostic factors are related to outcome. A multivariate model including baseline NSE, CgA, ECOG PS and Gleason score showed a 15% rise of the risk of death related to NSE (borderline P value). Conclusions: NSE was the most powerful predictor of survival for HRPC pts. Our results emphasize the theory that cells secreting NSE are chemoresistant, with a negative impact on OS. No significant financial relationships to disclose.

Download Full-text

Loss of miR-378 in prostate cancer, a common regulator of KLK2 and KLK4, correlates with aggressive disease phenotype and predicts the short-term relapse of the patients

Biological Chemistry ◽

10.1515/hsz-2014-0150 ◽

2014 ◽

Vol 395 (9) ◽

pp. 1095-1104 ◽

Cited By ~ 19

Author(s):

Margaritis Avgeris ◽

Konstantinos Stravodimos ◽

Andreas Scorilas

Keyword(s):

Prostate Cancer ◽

Gleason Score ◽

Cox Regression ◽

Elevated Serum ◽

Recurrence Risk ◽

Tumor Stage ◽

Short Term ◽

Prostate Hyperplasia ◽

Cox Regression Analysis ◽

Improve Risk Stratification

Abstract A large number of prostate cancer (PCa) patients receive treatment without significant benefits, strengthening the need for accurate prognosis, which can be supported by the study of miRNAs. In silico specificity analysis was performed for the identification of miRNAs able to regulate KLK2 and KLK4 expression. Total RNA was extracted from prostate tissues obtained from PCa and benign prostate hyperplasia patients. Thereafter, RNA was polyadenylated and reverse transcribed to cDNA, which was used for qPCR analysis. miR-378 was predicted to target both KLK2 and KLK4 and downregulated levels detected in PCa patients (p=0.050). The reduction of miR-378 was correlated with higher Gleason score (p=0.018), larger diameter tumors (p=0.034), and elevated serum PSA (p=0.006). Regarding prognosis, miR-378 was able to improve risk stratification according to Gleason score or tumor stage, while higher risk to recur highlighted for the patients expressing lower miR-378 levels. Finally, the loss of miR-378 was able to predict the short-term relapse of ‘high’- and ‘very high’-recurrence-risk patients, independent of Gleason score, tumor stage, PSA, and age as indicated by Kaplan-Meier survival curves (p=0.030) and multivariate Cox regression analysis (p=0.018). In conclusion, loss of miR-378 expression increases the risk for PCa progression and relapse, despite active treatment.

Download Full-text

Does perineural invasion in a radical prostatectomy specimen predict biochemical recurrence in men with prostate cancer?

Canadian Urological Association Journal ◽

10.5489/cuaj.2619 ◽

2015 ◽

Vol 9 (5-6) ◽

pp. 252 ◽

Cited By ~ 12

Author(s):

Fairleigh Reeves ◽

Christopher M. Hovens ◽

Laurence Harewood ◽

Shayne Battye ◽

Justin S. Peters ◽

...

Keyword(s):

Prostate Cancer ◽

Regression Analysis ◽

Radical Prostatectomy ◽

Gleason Score ◽

Biochemical Recurrence ◽

Perineural Invasion ◽

Cox Regression ◽

Clinicopathological Parameters ◽

Positive Surgical Margin ◽

Cox Regression Analysis

Introduction: The ability of perineural invasion (PNI) in radical prostatectomy (RP) specimens to predict biochemical recurrence (BCR) is unclear. This study investigates this controversial question in a large cohort.Methods: A retrospective analysis was undertaken of prospectively collected data from 1497 men who underwent RP (no neoadjuvant therapy) for clinically localized prostate cancer. The association of PNI at RP with other clinicopathological parameters was evaluated. The correlation of clinicopathological factors and BCR (defined as prostate-specific antigen [PSA] >0.2 ng/mL) was investigated with univariable and multivariable Cox regression analysis in 1159 men.Results: PNI-positive patients were significantly more likely to have a higher RP Gleason score, pT3 disease, positive surgical margins, and greater cancer volume (p < 0.0005). The presence of PNI significantly correlated with BCR on univariable (hazard ratio 2.30, 95% confidence interval 1.50–3.55, p < 0.0005), but not multivariable analysis (p = 0.602). On multivariable Cox regression analysis the only independent prognostic factors were preoperative PSA, RP Gleason score, pT-stage, and positive surgical margin status. These findings are limited by a relatively short follow-up time and retrospective study design.Conclusions: PNI at RP is not an independent predictor of BCR. Therefore, routine reporting of PNI is not indicated. Future research should be targeted at the biology of PNI to increase the understanding of its role in prostate cancer progression.

Download Full-text

PVT1 Expression Is a Predictor for Poor Survival of Prostate Cancer Patients

Technology in Cancer Research & Treatment ◽

10.1177/1533033820971610 ◽

2021 ◽

Vol 20 ◽

pp. 153303382097161

Author(s):

Jianhua Liu ◽

Yanqing Li ◽

Qiqi Zhang ◽

Chaoxiang Lv ◽

Mingwei Wang ◽

...

Keyword(s):

Prostate Cancer ◽

Cancer Patients ◽

Gleason Score ◽

Roc Curve ◽

Long Noncoding Rna ◽

Noncoding Rna ◽

Cox Regression ◽

Molecular Therapy ◽

Vital Status ◽

Cox Regression Analysis

Objective: Dysregulation of long noncoding RNA is associated with a variety of cancers and LncRNA has anticancer or carcinogenic activities. PVT1, as a long noncoding RNA, plays an important role in the development of cancer. Methods: We use R to download and analyze the data in TCGA database. ROC curve is generated to evaluate the significance of PVT1 expression for the diagnosis of prostate cancer. Chi-square test is used to test correlation between PVT1 expression and clinical pathological features. Survival curve and univariate and multivariate cox regression analysis is performed to compare differences in the effect on the survival rate between PVT1 high expression and low expression. Results: The expression of PTV1 in tumor tissues was significantly higher than that in normal tissues(P<2.2e-16). The difference of PTV1 expression was observed according to vital status (P = 0.0051) and Gleason score (P = 0.0012). The expression of PTV1 is significantly associated with T classification (P < 0.0001), N classification (P = 0.0499), PSA (P = 0.0001), Gleason Score (P < 0.0001), targeted molecular therapy (P = 0.0264) and vital status(P = 0.0036). The area under the ROC curve (AUC) was 0.860, which revealed PTV1 expression has excellent diagnostic value in prostate cancer. Patients with high PVT1 expression had a worse prognosis. Conclusions: PVT1 expression may be a biomarker for the diagnosis and prognosis of prostate cancer.

Download Full-text

Exploration of the Tumor Mutational Burden as a Prognostic Biomarker and Related Hub Gene Identification in Prostate Cancer

Technology in Cancer Research & Treatment ◽

10.1177/15330338211052154 ◽

2021 ◽

Vol 20 ◽

pp. 153303382110521

Author(s):

Licheng Wang ◽

Yicong Yao ◽

Chengdang Xu ◽

Xinan Wang ◽

Denglong Wu ◽

...

Keyword(s):

Prostate Cancer ◽

T Cells ◽

Cox Regression ◽

Prognostic Biomarker ◽

The Cancer Genome Atlas ◽

Hub Genes ◽

Cox Regression Analysis ◽

Mutational Burden ◽

Clinicopathologic Feature ◽

Tumor Mutational Burden

To explore the signature function of the tumor mutational burden (TMB) and potential biomarkers in prostate cancer (PCa), transcriptome profiles, somatic mutation data, and clinicopathologic feature information were downloaded from The Cancer Genome Atlas (TCGA) database. R software package was used to generate a waterfall plot to summarize the specific mutation information and calculate the TMB value of PCa. Least absolute shrinkage and selection operator (LASSO) Cox regression analysis was used to select the hub genes related to the TMB from the ImmPort network to build a risk score (RS) model to evaluate prognostic values and plot Kaplan–Meier (K-M) curves to predict PCa patients survival. The results showed that PCa patients with a high TMB exhibited higher infiltration of CD8+ T cells and CD4+ T cells and better overall survival (OS) than those with a low TMB. The anti-Mullerian hormone (AMH), baculoviral IAP repeat-containing 5 (BIRC5), and opoid receptor kappa 1 (OPRK1) genes were three hub genes and their copy number variation (CNV) was relatively likely to affect the infiltration of immune cells. Moreover, PCa patients with low AMH or BIRC5 expression had a longer survival time and lower cancer recurrence, while elevated AMH or BIRC5 expression favored PCa progression. In contrast, PCa patients with low OPRK1 expression had poorer OS in the early stage of PCa and a higher recurrent rate than those with high expression. Taken together, these results suggest that the TMB may be a promising prognostic biomarker for PCa and that AMH, OPRK1, and BIRC5 are hub genes affecting the TMB; AMH, OPRK1, and BIRC5 could serve as potential immunotherapeutic targets for PCa treatment.

Download Full-text

Identification of a Novel Immune-Related CpG Methylation Signature to Predict Prognosis in Stage II/III Colorectal Cancer

Frontiers in Genetics ◽

10.3389/fgene.2021.684349 ◽

2021 ◽

Vol 12 ◽

Author(s):

Feng Chen ◽

Lijuan Pei ◽

Siyao Liu ◽

Yan Lin ◽

Xinyin Han ◽

...

Keyword(s):

Colorectal Cancer ◽

Dna Methylation ◽

Cox Regression ◽

Characteristic Curve ◽

Cox Proportional Hazards ◽

The Cancer Genome Atlas ◽

Stage Ii ◽

Risk Scores ◽

Cox Regression Analysis ◽

Methylation Patterns

With the increasing incidence of colorectal cancer (CRC) and continued difficulty in treating it using immunotherapy, there is an urgent need to identify an effective immune-related biomarker associated with the survival and prognosis of patients with this disease. DNA methylation plays an essential role in maintaining cellular function, and changes in methylation patterns may contribute to the development of autoimmunity, aging, and cancer. In this study, we aimed to identify a novel immune-related methylated signature to aid in predicting the prognosis of patients with CRC. We investigated DNA methylation patterns in patients with stage II/III CRC using datasets from The cancer genome atlas (TCGA). Overall, 182 patients were randomly divided into training (n = 127) and test groups (n = 55). In the training group, five immune-related methylated CG sites (cg11621464, cg13565656, cg18976437, cg20505223, and cg20528583) were identified, and CG site-based risk scores were calculated using univariate Cox proportional hazards regression in patients with stage II/III CRC. Multivariate Cox regression analysis indicated that methylated signature was independent of other clinical parameters. The Kaplan–Meier analysis results showed that CG site-based risk scores could significantly help distinguish between high- and low-risk patients in both the training (P = 0.000296) and test groups (P = 0.022). The area under the receiver operating characteristic curve in the training and test groups were estimated to be 0.771 and 0.724, respectively, for prognosis prediction. Finally, stratified analysis results suggested the remarkable prognostic value of CG site-based risk scores in CRC subtypes. We identified five methylated CG sites that could be used as an efficient overall survival (OS)-related biomarker for stage II/III CRC patients.

Download Full-text